Overview

Dataset statistics

Number of variables12
Number of observations8771
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 MiB
Average record size in memory134.1 B

Variable types

DateTime1
Numeric11

Warnings

Dwutlenek_azotu is highly correlated with Tlenki_azotuHigh correlation
Tlenki_azotu is highly correlated with Dwutlenek_azotuHigh correlation
PM10 is highly correlated with PM_2_5 and 1 other fieldsHigh correlation
PM_2_5 is highly correlated with PM10 and 1 other fieldsHigh correlation
Benzen is highly correlated with PM10 and 1 other fieldsHigh correlation
Dwutlenek_azotu is highly correlated with Tlenki_azotu and 1 other fieldsHigh correlation
Tlenki_azotu is highly correlated with Dwutlenek_azotu and 1 other fieldsHigh correlation
PM10 is highly correlated with PM_2_5 and 2 other fieldsHigh correlation
PM_2_5 is highly correlated with PM10 and 2 other fieldsHigh correlation
Benzen is highly correlated with PM10 and 3 other fieldsHigh correlation
Tlenek_wegla is highly correlated with Dwutlenek_azotu and 4 other fieldsHigh correlation
Temperatura is highly correlated with BenzenHigh correlation
Dwutlenek_azotu is highly correlated with Tlenki_azotuHigh correlation
Tlenki_azotu is highly correlated with Dwutlenek_azotu and 1 other fieldsHigh correlation
PM10 is highly correlated with PM_2_5 and 2 other fieldsHigh correlation
PM_2_5 is highly correlated with PM10 and 1 other fieldsHigh correlation
Benzen is highly correlated with PM10 and 2 other fieldsHigh correlation
Tlenek_wegla is highly correlated with Tlenki_azotu and 2 other fieldsHigh correlation
PM_2_5 is highly correlated with Temperatura and 2 other fieldsHigh correlation
Tlenki_azotu is highly correlated with Dwutlenek_azotuHigh correlation
Temperatura is highly correlated with PM_2_5 and 2 other fieldsHigh correlation
Dwutlenek_azotu is highly correlated with Tlenki_azotuHigh correlation
Benzen is highly correlated with PM_2_5 and 1 other fieldsHigh correlation
PM10 is highly correlated with PM_2_5 and 2 other fieldsHigh correlation
Wilgotnosc is highly correlated with TemperaturaHigh correlation
Tlenek_wegla is highly skewed (γ1 = 22.82293213) Skewed
Data has unique values Unique

Reproduction

Analysis started2021-12-14 18:09:36.138754
Analysis finished2021-12-14 18:10:15.993162
Duration39.85 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

Data
Date

UNIQUE

Distinct8771
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size395.1 KiB
Minimum2020-01-01 01:00:00
Maximum2020-12-31 23:00:00
2021-12-14T19:10:16.255271image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:10:16.569019image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Dwutlenek_azotu
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1076
Distinct (%)12.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.76959852
Minimum4.1
Maximum170.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size395.1 KiB
2021-12-14T19:10:17.160307image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum4.1
5-th percentile18
Q133.3
median47.1
Q361.75
95-th percentile85.95
Maximum170.1
Range166
Interquartile range (IQR)28.45

Descriptive statistics

Standard deviation21.03571769
Coefficient of variation (CV)0.4313284982
Kurtosis0.4868743692
Mean48.76959852
Median Absolute Deviation (MAD)14.2
Skewness0.5883063475
Sum427758.1486
Variance442.5014186
MonotonicityNot monotonic
2021-12-14T19:10:17.540611image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41.530
 
0.3%
57.527
 
0.3%
53.927
 
0.3%
32.625
 
0.3%
28.725
 
0.3%
50.225
 
0.3%
41.924
 
0.3%
41.424
 
0.3%
40.524
 
0.3%
37.823
 
0.3%
Other values (1066)8517
97.1%
ValueCountFrequency (%)
4.11
< 0.1%
4.21
< 0.1%
4.81
< 0.1%
5.31
< 0.1%
5.41
< 0.1%
5.71
< 0.1%
61
< 0.1%
6.11
< 0.1%
6.22
< 0.1%
6.31
< 0.1%
ValueCountFrequency (%)
170.11
< 0.1%
150.71
< 0.1%
143.81
< 0.1%
139.31
< 0.1%
138.42
< 0.1%
134.91
< 0.1%
134.61
< 0.1%
130.11
< 0.1%
128.51
< 0.1%
127.51
< 0.1%

Tlenki_azotu
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3183
Distinct (%)36.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean144.8817081
Minimum0.1
Maximum931.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size395.1 KiB
2021-12-14T19:10:17.974219image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile30.55
Q172.75
median120.7
Q3188.2
95-th percentile353.05
Maximum931.5
Range931.4
Interquartile range (IQR)115.45

Descriptive statistics

Standard deviation102.56951
Coefficient of variation (CV)0.707953484
Kurtosis3.653503683
Mean144.8817081
Median Absolute Deviation (MAD)54.7
Skewness1.59624611
Sum1270757.461
Variance10520.50438
MonotonicityNot monotonic
2021-12-14T19:10:18.305947image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
73.712
 
0.1%
75.511
 
0.1%
52.611
 
0.1%
84.511
 
0.1%
0.111
 
0.1%
76.210
 
0.1%
126.910
 
0.1%
141.410
 
0.1%
35.410
 
0.1%
74.710
 
0.1%
Other values (3173)8665
98.8%
ValueCountFrequency (%)
0.111
0.1%
0.29
0.1%
0.31
 
< 0.1%
0.42
 
< 0.1%
0.52
 
< 0.1%
0.63
 
< 0.1%
0.72
 
< 0.1%
0.81
 
< 0.1%
0.93
 
< 0.1%
13
 
< 0.1%
ValueCountFrequency (%)
931.51
< 0.1%
858.71
< 0.1%
806.71
< 0.1%
721.51
< 0.1%
705.71
< 0.1%
6961
< 0.1%
688.21
< 0.1%
677.61
< 0.1%
677.31
< 0.1%
668.21
< 0.1%

PM10
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1484
Distinct (%)16.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.12744512
Minimum3
Maximum248.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size395.1 KiB
2021-12-14T19:10:18.638052image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile11.7
Q121.2
median30.9
Q349.1
95-th percentile93.65
Maximum248.3
Range245.3
Interquartile range (IQR)27.9

Descriptive statistics

Standard deviation26.92642752
Coefficient of variation (CV)0.6881723924
Kurtosis4.82919537
Mean39.12744512
Median Absolute Deviation (MAD)12.2
Skewness1.849183933
Sum343186.8212
Variance725.032499
MonotonicityNot monotonic
2021-12-14T19:10:19.272485image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25.937
 
0.4%
90.3721303736
 
0.4%
2333
 
0.4%
20.931
 
0.4%
25.831
 
0.4%
23.831
 
0.4%
21.730
 
0.3%
24.530
 
0.3%
14.530
 
0.3%
36.329
 
0.3%
Other values (1474)8453
96.4%
ValueCountFrequency (%)
39
0.1%
3.81
 
< 0.1%
3.98
0.1%
41
 
< 0.1%
4.31
 
< 0.1%
4.63
 
< 0.1%
4.71
 
< 0.1%
4.86
0.1%
4.91
 
< 0.1%
5.21
 
< 0.1%
ValueCountFrequency (%)
248.31
< 0.1%
227.81
< 0.1%
225.71
< 0.1%
2141
< 0.1%
211.51
< 0.1%
207.91
< 0.1%
207.71
< 0.1%
202.41
< 0.1%
201.81
< 0.1%
198.41
< 0.1%

PM_2_5
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct968
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.10723767
Minimum0
Maximum226.1
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size395.1 KiB
2021-12-14T19:10:19.991904image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.7
Q110.5
median17.1
Q329
95-th percentile62.25
Maximum226.1
Range226.1
Interquartile range (IQR)18.5

Descriptive statistics

Standard deviation19.94510955
Coefficient of variation (CV)0.8631542134
Kurtosis9.55353004
Mean23.10723767
Median Absolute Deviation (MAD)8.1
Skewness2.439144258
Sum202673.5816
Variance397.807395
MonotonicityNot monotonic
2021-12-14T19:10:21.120759image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.265
 
0.7%
15.248
 
0.5%
0.348
 
0.5%
11.347
 
0.5%
11.847
 
0.5%
9.146
 
0.5%
12.746
 
0.5%
946
 
0.5%
7.345
 
0.5%
10.544
 
0.5%
Other values (958)8289
94.5%
ValueCountFrequency (%)
02
 
< 0.1%
0.131
0.4%
0.265
0.7%
0.348
0.5%
0.429
0.3%
0.519
 
0.2%
0.65
 
0.1%
0.72
 
< 0.1%
0.83
 
< 0.1%
0.95
 
0.1%
ValueCountFrequency (%)
226.11
< 0.1%
207.91
< 0.1%
204.91
< 0.1%
194.91
< 0.1%
180.51
< 0.1%
157.51
< 0.1%
157.11
< 0.1%
156.81
< 0.1%
155.31
< 0.1%
152.71
< 0.1%

Benzen
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct429
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.161035856
Minimum0
Maximum21.1
Zeros44
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size395.1 KiB
2021-12-14T19:10:21.892414image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1
Q10.2
median0.5
Q31.4
95-th percentile4.4
Maximum21.1
Range21.1
Interquartile range (IQR)1.2

Descriptive statistics

Standard deviation1.702606853
Coefficient of variation (CV)1.466455015
Kurtosis18.05997323
Mean1.161035856
Median Absolute Deviation (MAD)0.3
Skewness3.468709563
Sum10183.44549
Variance2.898870097
MonotonicityNot monotonic
2021-12-14T19:10:22.201586image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.21483
16.9%
0.1999
 
11.4%
0.3959
 
10.9%
0.4596
 
6.8%
0.5439
 
5.0%
0.6314
 
3.6%
0.7273
 
3.1%
0.8257
 
2.9%
0.9194
 
2.2%
1161
 
1.8%
Other values (419)3096
35.3%
ValueCountFrequency (%)
044
 
0.5%
0.1999
11.4%
0.13928571433
 
< 0.1%
0.14137931031
 
< 0.1%
0.14285714291
 
< 0.1%
0.14285714292
 
< 0.1%
0.153
 
< 0.1%
0.15517241381
 
< 0.1%
0.15517241381
 
< 0.1%
0.15517241381
 
< 0.1%
ValueCountFrequency (%)
21.11
< 0.1%
19.71
< 0.1%
19.11
< 0.1%
18.51
< 0.1%
14.81
< 0.1%
14.52
< 0.1%
14.41
< 0.1%
13.81
< 0.1%
13.72
< 0.1%
13.61
< 0.1%

Tlenek_wegla
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct136
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9198350956
Minimum0.2
Maximum147
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size395.1 KiB
2021-12-14T19:10:22.474374image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.3
Q10.5
median0.6
Q30.9
95-th percentile1.5
Maximum147
Range146.8
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation3.465951432
Coefficient of variation (CV)3.768013907
Kurtosis674.7476894
Mean0.9198350956
Median Absolute Deviation (MAD)0.2
Skewness22.82293213
Sum8067.873623
Variance12.01281933
MonotonicityNot monotonic
2021-12-14T19:10:22.759183image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.51506
17.2%
0.41352
15.4%
0.61298
14.8%
0.7975
11.1%
0.8710
8.1%
0.3579
 
6.6%
0.9534
 
6.1%
1391
 
4.5%
1.1272
 
3.1%
1.2229
 
2.6%
Other values (126)925
10.5%
ValueCountFrequency (%)
0.274
 
0.8%
0.3579
 
6.6%
0.41352
15.4%
0.51506
17.2%
0.52095808381
 
< 0.1%
0.59940119761
 
< 0.1%
0.61298
14.8%
0.60060240961
 
< 0.1%
0.60242424241
 
< 0.1%
0.60487804881
 
< 0.1%
ValueCountFrequency (%)
1471
< 0.1%
113.81
< 0.1%
96.81
< 0.1%
77.31
< 0.1%
64.81
< 0.1%
63.91
< 0.1%
63.81
< 0.1%
60.81
< 0.1%
52.41
< 0.1%
51.52
< 0.1%

Kierunek_wiatru
Real number (ℝ≥0)

Distinct722
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean215.1459212
Minimum0
Maximum360
Zeros6
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size395.1 KiB
2021-12-14T19:10:23.037744image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile44
Q1161.3
median246
Q3278
95-th percentile328
Maximum360
Range360
Interquartile range (IQR)116.7

Descriptive statistics

Standard deviation90.20711891
Coefficient of variation (CV)0.4192834259
Kurtosis-0.5103017265
Mean215.1459212
Median Absolute Deviation (MAD)42
Skewness-0.7918574092
Sum1887044.875
Variance8137.324301
MonotonicityNot monotonic
2021-12-14T19:10:23.325766image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24587
 
1.0%
25285
 
1.0%
24780
 
0.9%
24679
 
0.9%
25178
 
0.9%
27977
 
0.9%
26277
 
0.9%
28077
 
0.9%
24475
 
0.9%
27774
 
0.8%
Other values (712)7982
91.0%
ValueCountFrequency (%)
06
 
0.1%
112
0.1%
210
0.1%
317
0.2%
410
0.1%
510
0.1%
616
0.2%
717
0.2%
86
 
0.1%
915
0.2%
ValueCountFrequency (%)
3604
 
< 0.1%
3597
 
0.1%
3589
0.1%
35712
0.1%
35621
0.2%
35510
0.1%
35414
0.2%
35313
0.1%
35215
0.2%
35113
0.1%

Predkosc_wiatru
Real number (ℝ≥0)

Distinct3184
Distinct (%)36.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.653479907
Minimum0.2583333333
Maximum7.626666667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size395.1 KiB
2021-12-14T19:10:23.706746image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.2583333333
5-th percentile0.4666666667
Q10.8233333333
median1.423333333
Q32.206666667
95-th percentile3.6725
Maximum7.626666667
Range7.368333333
Interquartile range (IQR)1.383333333

Descriptive statistics

Standard deviation1.045424592
Coefficient of variation (CV)0.6322572097
Kurtosis1.849503214
Mean1.653479907
Median Absolute Deviation (MAD)0.6683333333
Skewness1.235104653
Sum14502.67226
Variance1.092912578
MonotonicityNot monotonic
2021-12-14T19:10:24.083739image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.663333333316
 
0.2%
0.54516
 
0.2%
0.703333333315
 
0.2%
0.641666666715
 
0.2%
0.511666666714
 
0.2%
0.521666666714
 
0.2%
0.563333333314
 
0.2%
0.551666666714
 
0.2%
0.556666666714
 
0.2%
0.528333333313
 
0.1%
Other values (3174)8626
98.3%
ValueCountFrequency (%)
0.25833333331
< 0.1%
0.26166666671
< 0.1%
0.26333333331
< 0.1%
0.27166666671
< 0.1%
0.27333333331
< 0.1%
0.27833333332
< 0.1%
0.281
< 0.1%
0.28333333331
< 0.1%
0.28793103451
< 0.1%
0.29310344831
< 0.1%
ValueCountFrequency (%)
7.6266666671
< 0.1%
7.4116666671
< 0.1%
6.9066666671
< 0.1%
6.9033333331
< 0.1%
6.6783333331
< 0.1%
6.6316666671
< 0.1%
6.621
< 0.1%
6.6083333331
< 0.1%
6.4433333331
< 0.1%
6.4283333331
< 0.1%

Temperatura
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION

Distinct7165
Distinct (%)81.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.53214859
Minimum-6.131666667
Maximum32.035
Zeros1
Zeros (%)< 0.1%
Negative634
Negative (%)7.2%
Memory size395.1 KiB
2021-12-14T19:10:24.539520image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-6.131666667
5-th percentile-0.7833333333
Q14.298680556
median9.817528736
Q316.5725
95-th percentile24.0925
Maximum32.035
Range38.16666667
Interquartile range (IQR)12.27381944

Descriptive statistics

Standard deviation7.690324505
Coefficient of variation (CV)0.7301762257
Kurtosis-0.6419103009
Mean10.53214859
Median Absolute Deviation (MAD)6.039195402
Skewness0.319362951
Sum92377.47527
Variance59.14109099
MonotonicityNot monotonic
2021-12-14T19:10:24.820937image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.583755
 
0.1%
8.0515384625
 
0.1%
11.308974365
 
0.1%
6.8890972225
 
0.1%
6.9601388895
 
0.1%
3.4466666675
 
0.1%
3.3778472225
 
0.1%
6.5183333335
 
0.1%
9.5951923085
 
0.1%
8.0905128215
 
0.1%
Other values (7155)8721
99.4%
ValueCountFrequency (%)
-6.1316666671
< 0.1%
-6.1033333331
< 0.1%
-5.9651
< 0.1%
-5.9066666671
< 0.1%
-5.7483333331
< 0.1%
-5.441
< 0.1%
-5.3683333331
< 0.1%
-5.0233333331
< 0.1%
-4.9266666671
< 0.1%
-4.8883333331
< 0.1%
ValueCountFrequency (%)
32.0351
< 0.1%
31.918333331
< 0.1%
31.838333331
< 0.1%
31.816666671
< 0.1%
31.771666671
< 0.1%
31.71
< 0.1%
31.681
< 0.1%
31.448333331
< 0.1%
31.383333331
< 0.1%
31.346666671
< 0.1%

Wilgotnosc
Real number (ℝ≥0)

HIGH CORRELATION

Distinct7726
Distinct (%)88.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.2152353
Minimum8.856666667
Maximum85.37833333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size395.1 KiB
2021-12-14T19:10:25.093979image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum8.856666667
5-th percentile29.71
Q149.99541667
median64.57833333
Q375.095
95-th percentile81.79916667
Maximum85.37833333
Range76.52166667
Interquartile range (IQR)25.09958333

Descriptive statistics

Standard deviation16.59018731
Coefficient of variation (CV)0.271014025
Kurtosis-0.5029015431
Mean61.2152353
Median Absolute Deviation (MAD)11.855
Skewness-0.6621250448
Sum536918.8288
Variance275.234315
MonotonicityNot monotonic
2021-12-14T19:10:25.417128image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66.6831255
 
0.1%
51.400486115
 
0.1%
72.002115385
 
0.1%
64.514722225
 
0.1%
78.63225
 
0.1%
50.510555565
 
0.1%
69.577820515
 
0.1%
60.609861114
 
< 0.1%
66.119861114
 
< 0.1%
77.994
 
< 0.1%
Other values (7716)8724
99.5%
ValueCountFrequency (%)
8.8566666671
< 0.1%
9.371
< 0.1%
9.4451
< 0.1%
10.0951
< 0.1%
10.181666671
< 0.1%
10.271666671
< 0.1%
10.296666671
< 0.1%
10.61
< 0.1%
10.873333331
< 0.1%
10.931
< 0.1%
ValueCountFrequency (%)
85.378333331
< 0.1%
85.361666671
< 0.1%
85.358333331
< 0.1%
85.348333331
< 0.1%
85.333333331
< 0.1%
85.31
< 0.1%
85.291666672
< 0.1%
85.288333331
< 0.1%
85.266666671
< 0.1%
85.248333331
< 0.1%

Cisnienie
Real number (ℝ≥0)

Distinct7161
Distinct (%)81.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean990.3376553
Minimum958.8811321
Maximum1017.685
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size395.1 KiB
2021-12-14T19:10:25.776730image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum958.8811321
5-th percentile977.1158333
Q1985.6708333
median990.1458333
Q3995.7504167
95-th percentile1003.0225
Maximum1017.685
Range58.80386792
Interquartile range (IQR)10.07958333

Descriptive statistics

Standard deviation7.825081795
Coefficient of variation (CV)0.007901428117
Kurtosis0.420657137
Mean990.3376553
Median Absolute Deviation (MAD)5.119166667
Skewness-0.1332258287
Sum8686251.575
Variance61.2319051
MonotonicityNot monotonic
2021-12-14T19:10:26.108636image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
995.5056
 
0.1%
987.64701395
 
0.1%
987.34166675
 
0.1%
986.17034725
 
0.1%
986.91756945
 
0.1%
989.9155
 
0.1%
987.25659725
 
0.1%
984.1855
 
0.1%
991.2955
 
0.1%
987.92333335
 
0.1%
Other values (7151)8720
99.4%
ValueCountFrequency (%)
958.88113211
< 0.1%
959.16603771
< 0.1%
959.55849061
< 0.1%
959.85283021
< 0.1%
960.27358491
< 0.1%
960.81698111
< 0.1%
961.11886791
< 0.1%
961.98301891
< 0.1%
962.8452831
< 0.1%
963.45849061
< 0.1%
ValueCountFrequency (%)
1017.6851
< 0.1%
1017.621
< 0.1%
1017.6016671
< 0.1%
1017.5283331
< 0.1%
1017.5183331
< 0.1%
1017.5116671
< 0.1%
1017.4533331
< 0.1%
1017.4283331
< 0.1%
1017.4051
< 0.1%
1017.331
< 0.1%

Interactions

2021-12-14T19:09:42.872014image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:09:43.145984image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:09:43.440737image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:09:43.723979image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:09:43.962342image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:09:44.325397image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:09:44.559775image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:09:44.800111image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:09:45.042249image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:09:45.286624image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:09:45.532936image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:09:45.759329image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:09:45.985670image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:09:46.189129image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:09:46.407544image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:09:46.625995image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:09:46.869857image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:09:47.210941image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:09:47.668717image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:09:48.287062image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:09:48.754946image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:09:48.977865image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:09:49.210668image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:09:49.461822image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:09:49.672873image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:09:49.891984image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:09:50.091485image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:09:50.290953image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:09:50.507373image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:09:50.739753image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:09:51.105872image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:09:51.331274image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:09:51.554854image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:09:51.777791image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:09:51.995716image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:09:52.199948image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:09:52.418367image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:09:52.618828image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:09:52.876143image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:09:53.168357image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:09:53.398799image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:09:53.620785image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:09:53.841355image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:09:54.105647image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:09:54.317616image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:09:54.535047image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:09:54.734500image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:09:54.956949image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:09:55.157923image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:09:55.356636image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:09:55.590625image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:09:55.806048image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:09:56.020475image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:09:56.316684image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:09:56.555408image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:09:56.774821image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:09:57.027070image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:09:57.249511image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:09:57.475901image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:09:57.700302image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:09:57.931716image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:09:58.356572image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:09:58.624860image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:09:58.858678image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:09:59.126472image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:09:59.355856image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:09:59.657052image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:09:59.910386image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:10:00.239312image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:10:00.658234image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:10:01.042202image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:10:01.272585image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:10:01.516595image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:10:01.773906image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:10:02.026760image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:10:02.259280image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:10:02.541333image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:10:02.886410image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:10:03.127769image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:10:03.352299image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:10:03.574223image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:10:03.802617image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:10:04.026802image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:10:04.267159image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:10:04.509511image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:10:04.740046image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:10:04.994364image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:10:05.241252image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:10:05.488690image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:10:05.747532image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:10:06.068664image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:10:06.296059image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:10:06.522449image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:10:06.749875image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:10:07.002658image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:10:07.240596image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:10:07.476759image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:10:07.952030image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:10:08.291123image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:10:08.532477image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:10:08.790545image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:10:09.017938image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:10:09.249673image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:10:09.474742image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:10:09.701670image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:10:09.951539image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:10:10.204145image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:10:10.441512image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:10:10.685860image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:10:10.931201image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:10:11.174597image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:10:11.517692image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:10:11.775384image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:10:12.349365image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:10:12.873411image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:10:13.275241image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:10:13.647748image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:10:13.903066image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:10:14.167358image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:10:14.425180image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-14T19:10:14.731657image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2021-12-14T19:10:26.404097image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-12-14T19:10:26.876061image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-12-14T19:10:27.298860image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-12-14T19:10:27.698788image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-12-14T19:10:15.168201image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-12-14T19:10:15.766601image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

DataDwutlenek_azotuTlenki_azotuPM10PM_2_5BenzenTlenek_weglaKierunek_wiatruPredkosc_wiatruTemperaturaWilgotnoscCisnienie
02020-01-01 01:00:0019.735.034.317.90.20.7278.02.4716674.40333362.0583331002.798333
12020-01-01 02:00:0022.843.532.215.00.20.7279.02.2000004.11333361.3366671002.940000
22020-01-01 03:00:0031.868.435.716.30.20.8270.01.9066673.83000062.4100001003.261667
32020-01-01 04:00:0026.452.634.015.00.20.7278.01.9983333.48000063.5416671003.481667
42020-01-01 05:00:0024.550.426.111.60.20.7277.02.2483333.13500064.7866671003.888333
52020-01-01 06:00:0022.950.125.911.10.10.7276.01.7883332.81833365.6366671004.068333
62020-01-01 07:00:0021.943.422.610.60.20.7275.02.0883332.78500065.1666671004.423333
72020-01-01 08:00:0027.851.418.69.50.10.7271.02.2766673.34666760.1550001004.938333
82020-01-01 09:00:0024.643.220.310.10.20.6268.02.6100003.46333358.8500001004.996667
92020-01-01 10:00:0022.940.129.210.60.10.7277.02.6516673.54333359.3916671005.078333

Last rows

DataDwutlenek_azotuTlenki_azotuPM10PM_2_5BenzenTlenek_weglaKierunek_wiatruPredkosc_wiatruTemperaturaWilgotnoscCisnienie
87612020-12-31 14:00:0047.9171.449.020.41.71.0178.9666671.2451823.33799468.356238986.578516
87622020-12-31 15:00:0051.0169.652.321.71.61.0161.1000001.2871882.94397769.407799986.674006
87632020-12-31 16:00:0055.8188.051.824.52.51.1158.5666671.3155632.56993770.463420986.736211
87642020-12-31 17:00:0053.0166.349.725.92.21.1173.8666671.3177922.34343471.337405986.764173
87652020-12-31 18:00:0051.1142.549.227.22.61.0157.1666671.3256132.11196072.428350986.814570
87662020-12-31 19:00:0044.8162.252.328.92.41.1159.5172411.3516811.89314573.365169986.882812
87672020-12-31 20:00:0039.5108.152.630.12.70.9157.3103451.4288411.84234673.492614986.892811
87682020-12-31 21:00:0039.383.747.827.31.80.8173.0344831.4094851.77058973.711608986.900253
87692020-12-31 22:00:0041.3106.352.531.21.80.9142.1379311.4066951.69861673.832488986.820671
87702020-12-31 23:00:0039.391.266.741.93.41.1160.8965521.4372441.55477474.218842986.681872